A Multi‐Level Meta‐Analysis Comparing Relationship Norm Strength of <scp>LGB</scp> and Heterosexual Relationships
Bibliographic record
Abstract
ABSTRACT LGB people have historical and contemporary experiences of discrimination, stigma, and violence. Research on social groups suggests that norms, rules, and expectations for group conduct emerge to provide responses to group threats, for example, oppression of LGB individuals, and arise from cultural expectations and agreements between partners. While previous research demonstrated that norm content , specific rules, and expectations in relationships established by LGB people differ from their heterosexual counterparts, it is unclear if norm strength (i.e., when norms are clearly articulated, shared, binding) and its role for relationship satisfaction vary across sexual orientations. Using multilevel meta‐analysis, we synthesized effects from four independent samples (total N = 1171) containing data on sexual orientation, relationship norm strength, and satisfaction. We tested whether (a) norm strength and (b) its association with satisfaction were dependent on sexual orientation and/or particular forms of norm strength. Contrary to expectations, we found no differences in norm strength across sexual identities or forms of norm strength. Our findings suggest similar degrees of norm strength govern LGB relationships. Limitations include cross‐sectional MTurk samples and mono‐informant data that prevent examination of dyadic processes. Future research should distinguish which normative mechanisms (e.g., the content of/conformity to norms) are responsible for shaping relationships.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.024 | 0.054 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.030 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".